V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
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S2-Net is an oscillatory spiking neural network that uses time-delayed synchronization for bottom-up and top-down coordination to enable efficient, brain-inspired information processing across tasks like decoding and reasoning.
citing papers explorer
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Revisiting Feature Prediction for Learning Visual Representations from Video
V-JEPA models trained only on feature prediction from 2 million public videos achieve 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet-1K using frozen ViT-H/16 backbones.
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From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination
S2-Net is an oscillatory spiking neural network that uses time-delayed synchronization for bottom-up and top-down coordination to enable efficient, brain-inspired information processing across tasks like decoding and reasoning.